Forward Models That Integrate High-Dimensional and Localized Sensing of Peripheral Muscle Behavior Enable Task-Independent Prediction of Lower-Limb Joint Torque and Position Future States

Abstract

We develop a task-independent predictive framework that estimates hip, knee and ankle future behavior from sonomyographic sensing of quadriceps musculature. Two regression models, support vector regression and Gaussian process regression, were trained and tested such that no ambulation mode recognition was required. Sonomyography features of the anterior thigh musculature were extracted during the swing phase of level, incline and stair ambulation tasks as inputs to the two models for continuous prediction of the future stance phase hip, knee and ankle moments. Next, sonomyography features of the anterior thigh musculature were extracted during the stance phase and used to predict the following swing phase hip, knee and ankle angles. Leave-one-stride-out cross-validation is used to evaluate this continuous prediction framework. Additionally, initial, peak and terminal joint moment and angle parameters are extracted from trajectories and evaluated. Both regression models were able to accurately predict continuous future joint moments and angles, as well as initial, peak and terminal value parameters of future joint moments and angles. However, the support vector regression model required relatively lower computational cost. Thus, we recommend the support vector regression model as an optimal model for forward prediction of joint mechanics from sonomyographic sensing during ambulation.